Which integrations are available for this server?

Manages environment variables for the server configuration, as indicated by its inclusion in the .gitignore additions. Provides a web user interface with a friendly dashboard for interacting with datasets, viewing results, and controlling the autonomous analyst workflow. Used for orchestration of the autonomous analyst pipeline, connecting the various analysis components into a coherent workflow. Enables local LLM inference using llama3.2:1b for generating interpretive summaries of data analysis and explaining dataset statistics in plain English. Powers the data handling capabilities for analyzing tabular data and preparing it for anomaly detection and visualization. Serves as the foundation for the autonomous analyst server, with specific support for Python 3.12. Enables the anomaly detection functionality using Mahalanobis distance to identify outliers in datasets.

How do I use MCP Autonomous Analyst?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Autonomous Analyst analyze this sales data for anomalies and summarize key insights" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Autonomous Analyst

🧠 Overview

Autonomous Analyst is a local, agentic AI pipeline that:

Analyzes tabular data
Detects anomalies with Mahalanobis distance
Uses a local LLM (llama3.2:1b via Ollama) to generate interpretive summaries
Logs results to ChromaDB for semantic recall
Is fully orchestrated via the Model Context Protocol (MCP)

⚙️ Features

Component	Description
FastAPI Web UI	Friendly dashboard for synthetic or uploaded datasets
MCP Tool Orchestration	Each process step is exposed as a callable MCP tool
Anomaly Detection	Mahalanobis Distance-based outlier detection
Visual Output	Saved scatter plot of inliers vs. outliers
Local LLM Summarization	Insights generated using `llama3.2:1b` via Ollama
Vector Store Logging	Summaries are stored in ChromaDB for persistent memory
Agentic Planning Tool	A dedicated LLM tool (`autonomous_plan`) determines next steps based on dataset context
Agentic Flow	LLM + memory + tool use + automatic reasoning + context awareness

🧪 Tools Defined (via MCP)

Tool Name	Description	LLM Used
`generate_data`	Create synthetic tabular data (Gaussian + categorical)	❌
`analyze_outliers`	Label rows using Mahalanobis distance	❌
`plot_results`	Save a plot visualizing inliers vs outliers	❌
`summarize_results`	Interpret and explain outlier distribution using `llama3.2:1b`	✅
`summarize_data_stats`	Describe dataset trends using `llama3.2:1b`	✅
`log_results_to_vector_store`	Store summaries to ChromaDB for future reference	❌
`search_logs`	Retrieve relevant past sessions using vector search (optional LLM use)	⚠️
`autonomous_plan`	Run the full pipeline, use LLM to recommend next actions automatically	✅

🤖 Agentic Capabilities

Autonomy: LLM-guided execution path selection with autonomous_plan
Tool Use: Dynamically invokes registered MCP tools via LLM inference
Reasoning: Generates technical insights from dataset conditions and outlier analysis
Memory: Persists and recalls knowledge using ChromaDB vector search
LLM: Powered by Ollama with llama3.2:1b (temperature = 0.1, deterministic)

Related MCP server: MCP Prompt Enhancer

🚀 Getting Started

1. Clone and Set Up

git clone https://github.com/MadMando/mcp-autonomous-analyst.git
cd mcp-autonomous-analyst
conda create -n mcp-agentic python=3.11 -y
conda activate mcp-agentic
pip install uv
uv pip install -r requirements.txt

2. Start the MCP Server

mcp run server.py --transport streamable-http

3. Start the Web Dashboard

uvicorn web:app --reload --port 8001

Then visit: http://localhost:8000

🌐 Dashboard Flow

Step 1: Upload your own dataset or click Generate Synthetic Data
Step 2: The system runs anomaly detection on feature_1 vs feature_2
Step 3: Visual plot of outliers is generated
Step 4: Summaries are created via LLM
Step 5: Results are optionally logged to vector store for recall

📁 Project Layout

📦 autonomous-analyst/
├── server.py                  # MCP server
├── web.py                     # FastAPI + MCP client (frontend logic)
├── tools/
│   ├── synthetic_data.py
│   ├── outlier_detection.py
│   ├── plotter.py
│   ├── summarizer.py
│   ├── vector_store.py
├── static/                   # Saved plot
├── data/                     # Uploaded or generated dataset
├── requirements.txt
├── .gitignore
└── README.md

📚 Tech Stack

MCP SDK: mcp
LLM Inference: Ollama running llama3.2:1b
UI Server: FastAPI + Uvicorn
Memory: ChromaDB vector database
Data: pandas, matplotlib, scikit-learn

✅ .gitignore Additions

__pycache__/
*.pyc
*.pkl
.env
static/
data/

🙌 Acknowledgements

This project wouldn't be possible without the incredible work of the open-source community. Special thanks to:

Tool / Library	Purpose	Repository
🧠 Model Context Protocol (MCP)	Agentic tool orchestration & execution	modelcontextprotocol/python-sdk
💬 Ollama	Local LLM inference engine (`llama3.2:1b`)	ollama/ollama
🔍 ChromaDB	Vector database for logging and retrieval	chroma-core/chroma
🌐 FastAPI	Interactive, fast web interface	tiangolo/fastapi
⚡ Uvicorn	ASGI server powering the FastAPI backend	encode/uvicorn

💡 If you use this project, please consider starring or contributing to the upstream tools that make it possible.

This repo was created with the assistance of a local rag-llm using llama3.2:1b

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